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Found 9 papers, 1 papers with code
Revamping AI Models in Dermatology: Overcoming Critical Challenges for Enhanced Skin Lesion Diagnosis
For a clinical image, our model generates three outputs: a hierarchical prediction, an alert for out-of-distribution images, and a recommendation for dermoscopy if clinical image alone is insufficient for diagnosis.
Skin Lesion Recognition with Class-Hierarchy Regularized Hyperbolic Embeddings
Accordingly, the learned prototypes preserve the semantic class relations in the embedding space and we can predict the label of an image by assigning its feature to the nearest hyperbolic class prototype.
Out-of-Distribution Detection for Long-tailed and Fine-grained Skin Lesion Images
Through this approach, 1) First, we target the mixup amongst middle and tail classes to address the long-tail problem.
Flexible Sampling for Long-tailed Skin Lesion Classification
To address this, in this paper, we propose a curriculum learning-based framework called Flexible Sampling for the long-tailed skin lesion classification task.
Hierarchical Knowledge Guided Learning for Real-world Retinal Diseases Recognition
From a modeling perspective, most deep learning models trained on these datasets may lack the ability to generalize to rare diseases where only a few available samples are presented for training.
Early Melanoma Diagnosis with Sequential Dermoscopic Images
In this study, we propose a framework for automated early melanoma diagnosis using sequential dermoscopic images.
Leveraging Regular Fundus Images for Training UWF Fundus Diagnosis Models via Adversarial Learning and Pseudo-Labeling
We propose the use of a modified cycle generative adversarial network (CycleGAN) model to bridge the gap between regular and UWF fundus and generate additional UWF fundus images for training.
Synergic Adversarial Label Learning for Grading Retinal Diseases via Knowledge Distillation and Multi-task Learning
In addition, we conduct additional experiments to show the effectiveness of SALL from the aspects of reliability and interpretability in the context of medical imaging application.
Bridge the Domain Gap Between Ultra-wide-field and Traditional Fundus Images via Adversarial Domain Adaptation
We design a regularisation technique to regulate the domain adaptation.
